http://www.bibsonomy.org/burst/user/danieltBibSonomy BuRST Feed for /user/danielt2008-07-26T22:44:47+02:00http://www.bibsonomy.org/bibtex/20973dc6d00e0f92da1d04bb2ab1679d4/danieltdanielt2008-03-30T00:22:34+01:00dipl_literatur collaboration journalism <span style="color:#555555;">Dan <a href="http://www.bibsonomy.org/author/Gillmor">Gillmor</a> </span><em>O'Reilly Media , Inc.,, </em><em>Sebastopol, CA, </em><em>August2004. </em>Sun Mar 30 00:22:34 CET 2008Sebastopol, CAHardcoverAugustWe the Media: Grassroots Journalism by the People, for the People2004dipl_literatur collaboration journalism {Grassroots journalists are dismantling Big Media's monopoly on the news, transforming it from a lecture to a conversation. Not content to accept the news as reported, these readers-turned-reporters are publishing in real time to a worldwide audience via the Internet. The impact of their work is just beginning to be felt by professional journalists and the newsmakers they cover. In <i>We the Media: Grassroots Journalism by the People, for the People</i>, nationally known business and technology columnist Dan Gillmor tells the story of this emerging phenomenon, and sheds light on this deep shift in how we make and consume the news.<p><i>We the Media</i> is essential reading for all participants in the news cycle:<ul> <li>Consumers learn how they can become producers of the news. Gillmor lays out the tools of the grassroots journalist's trade, including personal Web journals (called weblogs or blogs), Internet chat groups, email, and cell phones. He also illustrates how, in this age of media consolidation and diminished reporting, to roll your own news, drawing from the array of sources available online and even over the phone.</li> <li>Newsmakers politicians, business executives, celebrities get a wake-up call. The control that newsmakers enjoyed in the top-down world of Big Media is seriously undermined in the Internet Age. Gillmor shows newsmakers how to successfully play by the new rules and shift from control to engagement.</li> <li>Journalists discover that the new grassroots journalism presents opportunity as well as challenge to their profession. One of the first mainstream journalists to have a blog, Gillmor says, "My readers know more than I do, and that's a good thing." In <i>We the Media</i>, he makes the case to his colleagues that, in the face of a plethora of Internet-fueled news vehicles, they must change or become irrelevant.</li></ul> At its core, <i>We the Media</i> is a book about people. People like Glenn Reynolds, a law professor whose blog postings on the intersection of technology and liberty garnered him enough readers and influence that he became a source for professional journalists. Or Ben Chandler, whose upset Congressional victory was fueled by contributions that came in response to ads on a handful of political blogs. Or Iraqi blogger Zayed, whose Healing Irag blog (healingiraq.blogspot.com) scooped Big Media. Or acridrabbit, who inspired an online community to become investigative reporters and discover that the dying Kaycee Nichols sad tale was a hoax. Give the people tools to make the news, <i>We the Media</i> asserts, and they will. <p>Journalism in the 21st century will be fundamentally different from the Big Media that prevails today. We the Media casts light on the future of journalism, and invites us all to be part of it. }March 2008http://www.bibsonomy.org/bibtex/2f043f26047aa50e16092745f75536dac/danieltdanielt2008-03-30T00:01:05+01:00dipl_literatur information_retrieval <span style="color:#555555;">Sergey <a href="http://www.bibsonomy.org/author/Brin">Brin</a> and L <a href="http://www.bibsonomy.org/author/Page">Page</a> </span><em>Computer Networks and ISDN Systems</em><em>Jan1998. </em>Sun Mar 30 00:01:05 CET 2008Computer Networks and ISDN SystemsJanThe anatomy of a large-scale hypertextual Web search engine1998dipl_literatur information_retrieval In this paper, we present Google, a prototype of a large-scale search engine which makes heavy use of the structure present in hypertext. Google is designed to crawl and index the Web efficiently and produce much more satisfying search results than existing systems. The prototype with a full text and hyperlink database of at least 24 million pages is available at http://google.stanford.edu/ To engineer a search engine is a challenging task. Search engines index tens to hundreds of millions of web pages involving a comparable number of distinct terms. They answer tens of millions of queries every day. Despite the importance of large-scale search engines on the web, very little academic research has been done on them. Furthermore, due to rapid advance in technology and web proliferation, creating a web search engine today is very different from three years ago. This paper provides an in-depth description of our large-scale web search engine -- the first such detailed public description we know of to date. Apart from the problems of scaling traditional search techniques to data of this magnitude, there are new technical challenges involved with using the additional information present in hypertext to produce better search results. This paper addresses this question of how to build a practical large-scale system which can exploit the additional information present in hypertext. Also we look at the problem of how to effectively deal with uncontrolled hypertext collections where anyone can publish anything they want. http://www.bibsonomy.org/bibtex/27da1127fc4836e2cf58e3073f1b888b2/danieltdanielt2008-03-29T23:59:34+01:00related_work dipl_literatur information_retrieval folksonomies <span style="color:#555555;">Andreas <a href="http://www.bibsonomy.org/author/Hotho">Hotho</a> and Robert <a href="http://www.bibsonomy.org/author/Jäschke">J&#228;schke</a> and Christoph <a href="http://www.bibsonomy.org/author/Schmitz">Schmitz</a> and Gerd <a href="http://www.bibsonomy.org/author/Stumme">Stumme</a> </span><em>Proceedings of the 3rd European Semantic Web Conference, </em><em>volume4011ofLNCS, </em><em>page411-426. </em><em>Budva, Montenegro, </em><em>Springer, </em><em>June2006. </em>Sat Mar 29 23:59:34 CET 2008Budva, MontenegroProceedings of the 3rd European Semantic Web Conference June411-426LNCSInformation Retrieval in Folksonomies: Search and Ranking40112006related_work dipl_literatur information_retrieval folksonomies http://www.bibsonomy.org/bibtex/2c86549355475331f563d0a3ba7816dab/danieltdanielt2008-03-29T23:59:25+01:00dipl_literatur information_retrieval <span style="color:#555555;">Jon M. <a href="http://www.bibsonomy.org/author/Kleinberg">Kleinberg</a> </span><em>Journal of the ACM</em><em>46(5):604--632</em>(<em>1999</em>)Sat Mar 29 23:59:25 CET 2008Journal of the ACM5604--632Authoritative sources in a hyperlinked environment461999dipl_literatur information_retrieval . The network structure of a hyperlinked environment can be a rich source of information about the content of the environment, provided we have effective means for understanding it. We develop a set of algorithmic tools for extracting information from the link structures of such environments, and report on experiments that demonstrate their effectiveness in a variety of contexts on the World Wide Web. The central issue we address within our framework is the distillation of broad search topics,...http://www.bibsonomy.org/bibtex/2ac49c33e114ca171db40cece6a0ae4d6/danieltdanielt2008-03-29T23:10:57+01:00ranking dipl_literatur pagerank <span style="color:#555555;">L. <a href="http://www.bibsonomy.org/author/Page">Page</a> and S. <a href="http://www.bibsonomy.org/author/Brin">Brin</a> and R. <a href="http://www.bibsonomy.org/author/Motwani">Motwani</a> and T. <a href="http://www.bibsonomy.org/author/Winograd">Winograd</a> </span><em>Proceedings of the 7th International World Wide Web Conference, </em><em>page161--172. </em><em>Brisbane, Australia, </em>(<em>1998</em>)Sat Mar 29 23:10:57 CET 2008Brisbane, AustraliaProceedings of the 7th International World Wide Web Conference161--172The PageRank citation ranking: Bringing order to the Web1998ranking dipl_literatur pagerank http://www.bibsonomy.org/bibtex/22aba01638afaaf40b888876de7459a76/danieltdanielt2008-03-29T20:14:33+01:00dipl_literatur outlook <span style="color:#555555;">D. <a href="http://www.bibsonomy.org/author/Ikeda">Ikeda</a> and T. <a href="http://www.bibsonomy.org/author/Fujiki">Fujiki</a> and M. <a href="http://www.bibsonomy.org/author/Okumura">Okumura</a> </span><em>AAAI Spring Symposium: Computational Approaches to Analyzing ..., </em>(<em>2006</em>)Sat Mar 29 20:14:33 CET 2008AAAI Spring Symposium: Computational Approaches to Analyzing ...Automatically Linking News Articles to Blog Entries2006dipl_literatur outlook http://www.bibsonomy.org/bibtex/25d494d0f2865c902369f296ab73897ba/danieltdanielt2008-03-25T13:01:21+01:00dipl_literatur similarity information_retrieval <span style="color:#555555;">R. L. <a href="http://www.bibsonomy.org/author/Cilibrasi">Cilibrasi</a> and P. M. B. <a href="http://www.bibsonomy.org/author/Vitanyi">Vitanyi</a> </span><em>Knowledge and Data Engineering, IEEE Transactions on</em><em>19(3):370--383</em>(<em>2007</em>)Tue Mar 25 13:01:21 CET 2008Knowledge and Data Engineering, IEEE Transactions on3370--383The Google Similarity Distance192007dipl_literatur similarity information_retrieval Words and phrases acquire meaning from the way they are used in society, from their relative semantics to other words and phrases. For computers, the equivalent of "society¿ is "database,¿ and the equivalent of "use¿ is "a way to search the database.¿ We present a new theory of similarity between words and phrases based on information distance and Kolmogorov complexity. To fix thoughts, we use the World Wide Web (WWW) as the database, and Google as the search engine. The method is also applicable to other search engines and databases. This theory is then applied to construct a method to automatically extract similarity, the Google similarity distance, of words and phrases from the WWW using Google page counts. The WWW is the largest database on earth, and the context information entered by millions of independent users averages out to provide automatic semantics of useful quality. We give applications in hierarchical clustering, classification, and language translation. We give examples to distinguish between colors and numbers, cluster names of paintings by 17th century Dutch masters and names of books by English novelists, the ability to understand emergencies and primes, and we demonstrate the ability to do a simple automatic English-Spanish translation. Finally, we use the WordNet database as an objective baseline against which to judge the performance of our method. We conduct a massive randomized trial in binary classification using support vector machines to learn categories based on our Google distance, resulting in an a mean agreement of 87 percent with the expert crafted WordNet categories.http://www.bibsonomy.org/bibtex/25382b90ba97d3e75b3fd9a79e933f4d5/danieltdanielt2008-03-25T12:37:52+01:00wordnet dipl_literatur <span style="color:#555555;">George A. <a href="http://www.bibsonomy.org/author/Miller">Miller</a> </span><em>Communications of the ACM</em><em>38(11):39-41</em>(<em>1995</em>)Tue Mar 25 12:37:52 CET 2008Communications of the ACM1139-41WordNet: a Lexical Database for English381995wordnet dipl_literatur http://www.bibsonomy.org/bibtex/222df1595d65ca56ecc7828ebe99e09e2/danieltdanielt2008-03-24T23:09:53+01:00dipl_literatur blogosphere <span style="color:#555555;">Mike <a href="http://www.bibsonomy.org/author/Thelwall">Thelwall</a> </span><em>Proceedings of the 15th International WWW Conference: 3rd Annual Workshop on Weblogging Ecosystem: Aggregation, Analysis and Dynamics, </em><em>Edinburgh, UK, </em><em>May2006. </em>Mon Mar 24 23:09:53 CET 2008Edinburgh, UKProceedings of the 15th International {WWW} Conference: 3rd Annual Workshop on Weblogging Ecosystem: Aggregation, Analysis and DynamicsMayBloggers during the {L}ondon attacks: Top Information Sources and Topics2006dipl_literatur blogosphere http://www.bibsonomy.org/bibtex/2fdca87747a451a608791fc234073f0e0/danieltdanielt2008-03-21T17:15:46+01:00related_work visualization som dipl_literatur <span style="color:#555555;">Teuvo <a href="http://www.bibsonomy.org/author/Kohonen">Kohonen</a> </span><em>Springer, </em><em>Berlin, </em>(<em>2001</em>)Fri Mar 21 17:15:46 CET 2008BerlinSelf-Organizing Maps2001related_work visualization som dipl_literatur http://www.bibsonomy.org/bibtex/2b08749f1704199f95ddcda488d03ae40/danieltdanielt2008-03-21T17:13:55+01:00related_work som visualization dipl_literatur <span style="color:#555555;">Teuvo <a href="http://www.bibsonomy.org/author/Kohonen">Kohonen</a> </span><em>Biological Cybernetics</em>(<em>1982</em>)Fri Mar 21 17:13:55 CET 2008Biological Cybernetics59--69Self-Organized Formation of Topologically Correct Feature Maps431982related_work som visualization dipl_literatur http://www.bibsonomy.org/bibtex/2b71bc3206b0b9bb4dcd0b3c6dfc8f3b5/danieltdanielt2008-03-21T15:35:49+01:00matrix_factorization related_work dipl_literatur <span style="color:#555555;">D. D. <a href="http://www.bibsonomy.org/author/Lee">Lee</a> and H. S. <a href="http://www.bibsonomy.org/author/Seung">Seung</a> </span><em>Nature</em><em>401(6755):788--791</em><em>October1999. </em>Fri Mar 21 15:35:49 CET 2008Bell Laboratories, Lucent Technologies, Murray Hill, New Jersey 07974, USA.NatureOctober6755788--791Learning the parts of objects by non-negative matrix factorization.4011999matrix_factorization related_work dipl_literatur Is perception of the whole based on perception of its parts? There is psychological and physiological evidence for parts-based representations in the brain, and certain computational theories of object recognition rely on such representations. But little is known about how brains or computers might learn the parts of objects. Here we demonstrate an algorithm for non-negative matrix factorization that is able to learn parts of faces and semantic features of text. This is in contrast to other methods, such as principal components analysis and vector quantization, that learn holistic, not parts-based, representations. Non-negative matrix factorization is distinguished from the other methods by its use of non-negativity constraints. These constraints lead to a parts-based representation because they allow only additive, not subtractive, combinations. When non-negative matrix factorization is implemented as a neural network, parts-based representations emerge by virtue of two properties: the firing rates of neurons are never negative and synaptic strengths do not change sign.DBLP Record 'conf/ecir/Osinski06'http://www.bibsonomy.org/bibtex/2a2f6f1ef18fb7a56adb8f17d4a78814d/danieltdanielt2008-03-21T15:30:12+01:00related_work dipl_literatur <span style="color:#555555;">Stanislaw <a href="http://www.bibsonomy.org/author/Osinski">Osinski</a> </span><em>ECIR, </em><em>page167-178. </em>(<em>2006</em>)Fri Mar 21 15:30:12 CET 2008ECIRConfEcir2006167-178Improving Quality of Search Results Clustering with Approximate Matrix Factorisations2006related_work dipl_literatur DBLP Record 'conf/ecir/Osinski06'http://www.bibsonomy.org/bibtex/244251e99f468eeebf0dbecad230a737f/danieltdanielt2008-03-21T15:29:18+01:00proceedings <span style="color:#555555;">Mounia <a href="http://www.bibsonomy.org/author/Lalmas">Lalmas</a> and Andy <a href="http://www.bibsonomy.org/author/MacFarlane">MacFarlane</a> and Stefan M. <a href="http://www.bibsonomy.org/author/R{\&#034;u}ger">R&#252;ger</a> and Anastasios <a href="http://www.bibsonomy.org/author/Tombros">Tombros</a> and Theodora <a href="http://www.bibsonomy.org/author/Tsikrika">Tsikrika</a> and Alexei <a href="http://www.bibsonomy.org/author/Yavlinsky">Yavlinsky</a> (eds.). </span><em>volume3936ofLecture Notes in Computer Science, </em><em>Springer, </em>(<em>2006</em>)Fri Mar 21 15:29:18 CET 2008ECIRLecture Notes in Computer ScienceAdvances in Information Retrieval, 28th European Conference on IR Research, ECIR 2006, London, UK, April 10-12, 2006, Proceedings39362006proceedings http://www.bibsonomy.org/bibtex/2f5ad125d8b17af1fa22cc38d2ce91af8/danieltdanielt2008-03-21T15:07:03+01:00dipl_literatur lsa <span style="color:#555555;">S. <a href="http://www.bibsonomy.org/author/Deerwester">Deerwester</a> and S. T. <a href="http://www.bibsonomy.org/author/Dumais">Dumais</a> and G. W. <a href="http://www.bibsonomy.org/author/Furnas">Furnas</a> and T. K. <a href="http://www.bibsonomy.org/author/Landauer">Landauer</a> and R. <a href="http://www.bibsonomy.org/author/Harshman">Harshman</a> </span><em>Journal of the American Society of Information Science</em>(<em>1990</em>)Fri Mar 21 15:07:03 CET 2008Journal of the American Society of Information Science391--407Indexing by Latent Semantic Analysis411990dipl_literatur lsa DBLP Record 'conf/iis/OsinskiSW04'http://www.bibsonomy.org/bibtex/240aba631c1ac8819bd64b0ee74bfdd1b/danieltdanielt2008-03-21T15:04:49+01:00carrot2 related_work dipl_literatur <span style="color:#555555;">Stanislaw <a href="http://www.bibsonomy.org/author/Osinski">Osinski</a> and Jerzy <a href="http://www.bibsonomy.org/author/Stefanowski">Stefanowski</a> and Dawid <a href="http://www.bibsonomy.org/author/Weiss">Weiss</a> </span><em>Intelligent Information Systems, </em><em>page359-368. </em>(<em>2004</em>)Fri Mar 21 15:04:49 CET 2008Intelligent Information SystemsConfIis2004359-368Lingo: Search Results Clustering Algorithm Based on Singular Value Decomposition2004carrot2 related_work dipl_literatur DBLP Record 'conf/iis/OsinskiSW04'http://www.bibsonomy.org/bibtex/20b139d1624610e3fce4502e2e62cbd6d/danieltdanielt2008-03-21T15:04:05+01:00proceedings <span style="color:#555555;">Mieczyslaw A. <a href="http://www.bibsonomy.org/author/Klopotek">Klopotek</a> and Slawomir T. <a href="http://www.bibsonomy.org/author/Wierzchon">Wierzchon</a> and Krzysztof <a href="http://www.bibsonomy.org/author/Trojanowski">Trojanowski</a> (eds.). </span><em>Springer, </em>(<em>2004</em>)Fri Mar 21 15:04:05 CET 2008Intelligent Information SystemsAdvances in Soft ComputingIntelligent Information Processing and Web Mining, Proceedings of the International IIS: IIPWM'04 Conference held in Zakopane, Poland, May 17-20, 20042004proceedings dblphttp://www.bibsonomy.org/bibtex/24e70fa6a2cb3b31fbcaa325ffa79edb9/danieltdanielt2008-03-21T14:29:47+01:00related_work gng dipl_literatur <span style="color:#555555;">Bernd <a href="http://www.bibsonomy.org/author/Fritzke">Fritzke</a> </span><em>NIPS, </em><em>page625-632. </em><em>MIT Press, </em>(<em>1994</em>)Fri Mar 21 14:29:47 CET 2008NIPSConfNips1994625-632A Growing Neural Gas Network Learns Topologies.1994related_work gng dipl_literatur http://www.bibsonomy.org/bibtex/24a3e685e40bf0c9ecf1251b379abe4d7/danieltdanielt2008-03-21T14:29:29+01:00related_work dipl_literatur gng <span style="color:#555555;">Bernd <a href="http://www.bibsonomy.org/author/Fritzke">Fritzke</a> </span><em>Neural Networks</em>(<em>1994</em>)Fri Mar 21 14:29:29 CET 2008Neural Networks1441--1460Growing Cell Structures---A Self-Organizing Network for Unsupervised and Supervised Learning71994related_work dipl_literatur gng DBLP Record 'conf/nips/Fritzke94'http://www.bibsonomy.org/bibtex/26ce16214cd2d378869b6769cf3720ab7/danieltdanielt2008-03-21T14:27:43+01:00proceedings <span style="color:#555555;">Gerald <a href="http://www.bibsonomy.org/author/Tesauro">Tesauro</a> and David S. <a href="http://www.bibsonomy.org/author/Touretzky">Touretzky</a> and Todd K. <a href="http://www.bibsonomy.org/author/Leen">Leen</a> (eds.). </span><em>MIT Press, </em>(<em>1995</em>)Fri Mar 21 14:27:43 CET 2008NIPSAdvances in Neural Information Processing Systems 7, [NIPS Conference, Denver, Colorado, USA, 1994]1995proceedings